Comparison of human cord blood engraftment between immunocompromised mouse strains
Bibliographic record
Abstract
The nonobese diabetic/severe combined immune deficiency (NOD-scid) xenotransplantation model is the "gold standard" for assaying human hematopoietic stem cell activity. Systematic advancements, such as depletion of natural killer cell activity with anti-CD122 antibody, direct intrafemoral injection, and deletion or truncation of IL2Rgamma, have improved human cell engraftment; however, questions remain whether these mouse models are equivalent or, if not, which model is superior for assaying hematopoietic stem cell activity. To address this, we compared overall engraftment and multilineage differentiation of near-limiting doses of lineage-depleted human umbilical cord blood cells by direct intrafemoral injection into NOD/Lt-scid, NOD/Shi-scid, NOD/Lt-scid/IL2Rgamma(null) (NSG), and NOD/Shi-scid/IL2Rgamma(null) mice. Transplantation into NSG mice generated moderately higher human engraftment levels in bone marrow compared with other strains. At limiting doses, NSG mice of both sexes were 3.6-fold more sensitive in detecting SCID-repopulating cells compared with NOD/Lt-scid mice. However, NSG females exhibited higher engraftment at limiting cell doses, resulting in an overall increase in SCID-repopulating cell detection of 9-fold. Both NSG and NOD/Shi-scid/IL2Rgamma(null) support significantly improved engraftment in peripheral tissues compared with NOD/Lt-scid and NOD/Shi-scid mice, whereas NSG mice provide greater human engraftment in bone marrow than all other strains, especially at limiting doses.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".